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Journal of Applied Information, Communication and Technology (JAICT)
Published by Politeknik Negeri Semarang
ISSN : 25416340     EISSN : 25416359     DOI : https://doi.org/10.32497/jaict
Core Subject : Engineering,
Engineering
Focus of JAICT: Journal of Applied Information and Communication Technologies is published twice per year and is committed to publishing high-quality articles that advance the practical applications of communication and information technologies. JAICT scope covers all aspects of theory, application and design of communication and information technologies, including (but not limited): Communication and Information Theory. Mobile and Wireless Communication, Cognitive Radio Networks. Ad Hoc, Mesh, Wireless Sensor Network, Distributed System and cloud computing Computer networking and IoT Optimization Algorithms, Artificial intelligence, Machine Learning, and Adaptive System.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 101 Documents
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Real-time IoT-Based Monitoring and ANN-Driven Prediction of Electrical Parameters in a 1200 W Photovoltaic System Eko Supriyanto; Abu Hasan; Tulus Pramuji; Tri Raharjo Yudantoro; Khamami; Roni Apriantoro; Hanny Nurrani
JAICT Vol. 12 No. 1 (2026): JAICT
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing adoption of small-scale photovoltaic (PV) systems underscores the necessity for advanced monitoring that improves dependability and maintenance efficacy. Conventional photovoltaic monitoring emphasizes real-time data acquisition but is deficient in predictive capabilities, hindering early defect identification and proactive maintenance. This study introduces an IoT-enabled real-time monitoring system utilizing ANN-based predictions for a 1200 W photovoltaic configuration. Voltage and current sensors interface with an ESP32 microcontroller to quantify and analyze voltage, current, power, and energy. Data is transmitted to the cloud using MQTT, enabling users to remotely monitor and display information through web or mobile applications. A feedforward Artificial Neural Network (ANN) taught by backpropagation enhances intelligence by predicting future electrical performance based on historical data. Experimental findings indicate a consistent communication latency of 1-2 seconds and sensor inaccuracies below 3%. The ANN model attained a MAPE of 3-4%. The integration of IoT monitoring with ANN prediction facilitates early anomaly detection, enhances operational understanding, and enables scalable, intelligent energy management.

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